Interactive error repair for an online handwriting interface

Huerst, Wolfgang; Yang, Jie; Waibel, Alex

doi:10.1145/286498.286818

Cited by 9 publications

(7 citation statements)

References 1 publication

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“…This in turn challenges the design of the stroke-based text-editing UI and of course the underlying gesture recognizer. (Huerst et al, 2010;Leiva et al, 2013). Otherwise, if a gesture is misrecognized as text or vice versa, cascading errors (Karat et al, 1999) are likely to happen.…”

Section: The Catti Frameworkmentioning

confidence: 99%

Context-Aware Gestures for Mixed-Initiative Text Editing UIs

et al. 2014

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Oxford University PressLeiva, LA.; Alabau, V.; Romero Gómez, V.; Toselli, AH.; Vidal, E. (2015). Contex-aware gestures for mixed-initiative text editings UIs. Interacting with Computers. 27(6):675-696. doi:10.1093/iwc/iwu019. +34 963878172 Email: {luileito,valabau,vromero,ahector,evidal}@prhlt.upv.es This work is focused on enhancing highly interactive text-editing applications with gestures. Concretely, we study CATTI, a handwriting transcription system that follows a corrective feedback paradigm, where both the user and the system collaborate efficiently to produce a high-quality text transcription. CATTI-like applications demand fast and accurate gesture recognition, for which we observed that current gesture recognizers are not adequate enough. In response to this need we developed MinGestures, a parametric context-aware gesture recognizer. Our contributions include a number of stroke features for disambiguating copy-mark gestures from handwritten text, plus the integration of these gestures in a CATTI application. It becomes finally possible to create highly interactive stroke-based textediting interfaces, without worrying to verify the user intent onscreen.We performed a formal evaluation with 22 e-pen users and 32 mouse users using a gesture vocabulary of 10 symbols. MinGestures achieved an outstanding accuracy (less than 1% error rate) with very high performance (less than 1 ms of recognition times). We then integrated MinGestures in a CATTI prototype and tested the performance of the interactive handwriting system when it is driven by gestures. Our results show that using gestures in interactive handwriting applications is both advantageous and convenient when gestures are simple but context-aware. Taken together, this work suggests that text-editing interfaces not only can be easily augmented with simple gestures, but also may substantially improve user productivity.

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Section: The Catti Frameworkmentioning

confidence: 99%

Context-Aware Gestures for Mixed-Initiative Text Editing UIs

et al. 2014

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“…Huerst et al [68] have experimented with a handwriting pre-processor which looks for user correction marks on their own handwriting, such as crossing out a letter, and applies these corrections before sending handwriting to a recogniser. As speech recognition systems become more sophisticated and capable of recognising spontaneous speech, they will also eventually become capable of finding in user speech natural marks of self-correction such as pauses and word repetition.…”

Section: User Correctionmentioning

confidence: 99%

Towards a taxonomy of error-handling strategies in recognition-based multi-modal human–computer interfaces

Bourguet

2006

Signal Processing

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In this paper, we survey the different types of error-handling strategies that have been described in the literature on recognition-based human-computer interfaces. A wide range of strategies can be found in spoken human-machine dialogues, handwriting systems, and multimodal natural interfaces.We then propose a taxonomy for classifying error-handling strategies that has the following three dimensions: the main actor in the error-handling process (machine versus user), the purpose of the strategy (error prevention, discovery, or correction), and the use of different modalities of interaction. The requirements that different error-handling strategies have on different sets of interaction modalities are also discussed. The main aim of this work is to establish a classification that can serve as a tool for understanding how to develop more efficient and more robust multimodal human-machine interfaces.Keywords: recognition-based technology, multimodal interfaces, error-handling, taxonomy, interaction design, interaction robustness Recognition-based technologyMultimodal interaction refers to interaction with the virtual and physical environment through natural modes of communication such as speech, body gestures, handwriting, graphics, or gaze.Unlike keyboards and mice inputs, natural modes of communication usually are non-deterministic, and have to be "recognised" by a recognition system, before they can be passed on to an application.Recent developments in recognition-based technology (e.g. speech and gesture recognition) have opened a myriad of new possibilities for the design and implementation of multimodal applications.Handwriting recognisers, for example, are being used in personal digital assistants (e.g. Paragon's multilingual PenReader software for Pocket PC devices), and speech recognition has made its way into desktop machines (e.g. IBM's ViaVoice TM speech recognition engines). However, designing and

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“…A subsequent investigation described three repair patterns, these being deletion, completion / insertion and overwriting (Huerst et al, 1998).…”

Section: Errors In Disobedient Systemsmentioning

confidence: 99%

“…Recovery is aided by clarifying the cause of the error; making the remedy easy to find, and by providing easy to use correction tools When a child first noticed an error, he or she attempted one of the three repair strategies of deletion, completion/insertion or overwriting (Huerst et al, 1998). Depending on when the error was noted, children either rubbed out all or some of the word and then re-wrote, or, if they were part way through a word, they sometimes attempted to overwrite on, or scribble out the word using, the pen and graphics tablet.…”

Section: Error Recoverymentioning

confidence: 99%

Oops! silly me! errors in a handwriting recognition-based text entry interface for children

Read¹,

MacFarlane²,

Casey³

2002

Proceedings of the Second Nordic Conference on Human-Computer Interaction - NordiCHI '02

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This paper describes an empirical study in which children aged 7 and 8 used handwriting recognition software and hardware to input their own unconstrained text into the computer. The children were observed using the software, and the behaviour of both the children and the system is described.Handwriting recognition is a 'disobedient' technology; that is, it behaves erroneously, sometimes failing to generate correct representations of the child's intentions. This presents problems for the child, and these problems, and the strategies which the children adopted, are considered. Previous work on error correction with disobedient interfaces is used to provide grounding for the discussion.Two models are proposed, one describing user-states, the second introducing the notion of 'tidal' error repair. These models are then used to suggest some strategies for the design of more usable handwriting recognition interfaces for children.

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Interactive error repair for an online handwriting interface

Cited by 9 publications

References 1 publication

Context-Aware Gestures for Mixed-Initiative Text Editing UIs

Context-Aware Gestures for Mixed-Initiative Text Editing UIs

Towards a taxonomy of error-handling strategies in recognition-based multi-modal human–computer interfaces

Oops! silly me! errors in a handwriting recognition-based text entry interface for children

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